Hybrid Harmony Search Algorithm to Solve the Feature Selection for Data Mining Applications

Abualigah, Laith; Al‐diabat, Mofleh; Dhou, Khaldoon; Alsalibi, Bisan; Hanandeh, Essam Said; Shehab, Mohammad

doi:10.1002/9781119551621.ch2

Cited by 13 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the given search field, these functions have a single final solution. Meanwhile, the Simple Multimodal Functions (from F3 to F9 in Table 2) test the efficiency of the proposed algorithms in evading the exploitation optimum and providing the global solution [50]. The third form is Hybrid Functions (from F10 to F12 in Table 2), which evaluates the proposed algorithms' optimization efficiency and convergence acceleration.…”

Section: Part 1: Experiments On Cec 2015mentioning

confidence: 99%

Opposition-based learning multi-verse optimizer with disruption operator for optimization problems

Shehab

Abualigah

2022

Soft Comput

Self Cite

View full text Add to dashboard Cite

Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

show abstract

Section: Part 1: Experiments On Cec 2015mentioning

confidence: 99%

Opposition-based learning multi-verse optimizer with disruption operator for optimization problems

Shehab

Abualigah

2022

Soft Comput

Self Cite

View full text Add to dashboard Cite

show abstract

“…Kumar and Kumar in [96] 4.1. Data Clustering Applications K-means clustering is a commonly utilized technique for data and text clustering, used in the data-mining domain due to its homogeneity and high velocity in grouping massive datasets [97][98][99]. However, K-means has two main weaknesses, one involving the initial centroid and the other involving convergence to the exploration area.…”

Section: Hsa Clustering Gene Expressionmentioning

confidence: 99%

A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications

2020

View full text Add to dashboard Cite

The Harmony Search Algorithm (HSA) is a swarm intelligence optimization algorithm which has been successfully applied to a broad range of clustering applications, including data clustering, text clustering, fuzzy clustering, image processing, and wireless sensor networks. We provide a comprehensive survey of the literature on HSA and its variants, analyze its strengths and weaknesses, and suggest future research directions.

show abstract

“…text clustering is used in several fields such as text mining, data processing, pattern recognition, image clustering. This chapter [79] demonstrated the methodology projected hybrid function section technique assisted by the harmony search principle for the method of text agglomeration. Irregular harmony memory is generated by the harmony search principle, which includes a list of candidate solutions.…”

Section: Harmony Search (Hs)mentioning

confidence: 99%

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

et al. 2020

View full text Add to dashboard Cite

Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems.

show abstract

Hybrid Harmony Search Algorithm to Solve the Feature Selection for Data Mining Applications

Cited by 13 publications

References 34 publications

Opposition-based learning multi-verse optimizer with disruption operator for optimization problems

Opposition-based learning multi-verse optimizer with disruption operator for optimization problems

A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

Contact Info

Product

Resources

About